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■ ^ Agriculture 



Canada 

Research Direction generate 
Branch de la recherche 

Technical bulletin 1984-1 E 




The versatile soil moisture 
budget — version three 




Canada 



The map imi the ct)\rr has dots representing 
AKrit-uhure Canada research estabhshments. 



CAMADA AOftfCULTURC 

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RESEARCH STATION 

STATION DE RECMt.^CHES C 



2 LETHBRIDGE. ALTA. f. 

O CANADA AGRICULTURE 3 



The versatile soil moisture 
budget — version three 



J. A. DYER 

Resources and Environment Section 
Crop Production Division 
Regional Development Branch 
Agriculture Canada 
Ottawa, Ontaiio 

A. R. MACK 

Agrometeorology Section 

Land Resource Research Institute 

Research Branch 

Agriculture Canada 

Ottawa, Ontario 

LRRI Contribution No. 82-33 



Research Branch 
Agiiculture Canada 
1984 



Cxjpies of this publication are available from: 

Agrometforologv ^ftion 

I«iiui Rfs()uicT Research Iiistitiite 

Rtstaiih Blanch 

Agmulturc C-inada 

Ottawa, Ontario 

K1A0C6 

Prcxiucfil b\ RfM'arch Program Service 

©Minister of Supply ami Ser\ iies Canada 1984 

Cat. No. A54— 8 1984— IE ISBN 0-662-13031-6 

Egalement disponible en francjais sous le litre 

Le bilan hydnque du sol polyvalent — version trois 



TABLE OF CONTENTS 



Preface 



Introduction 



PAGE 



Evapotranspi ration 

- Root Extraction 

- Crop Coefficients Adjustment 

- Soil Water Retention 

- Alternate Drying Curves 

Infiltration and Drainage 

- Drainage Rate Parameters 



4 
7 
8 
8 
11 

11 
13 



Snow Budget 

- Snowpack Losses 

- Snowmelt Infiltration 



14 
16 
16 



Field Performance Appraisal 
- Results 



17 
19 



Summary 
References 



22 
23 



Appendix A 
Appendix B 
Appendix C 
Appendix D 
Appendix E 



VBIII Program 

Zonal Parameters Adjustment Program 

Run Control Data 

Output Data 

Evapotranspi ration Coefficients (k, Z) 



- 2 - 



PREFACE 



Soil water is an important topic in agriculture, forestry and hydrology. 
It plays a significant role in plant growth, in determining crop yields and in 
the hydrological state of a region. It affects farm operations, cultural 
practices, harvesting conditions, irrigation requirements and water supplies. 
To monitor the dynamic changes in the water content of soils, non-destructive 
methods of sampling are required. They must provide repetitive, timely and 
reliable information under a wide range of climatic, vegetative and 
physiographic conditions, as well as cultural practices. Weather based 
computer simulation models have proved to be an effective soil water 
monitoring technique. 

Various soil moisture models have been proposed taking into account a 
number of biophysical factors. Many of these models follow the daily water 
budgeting approach. Budgets are a compromise between statistical and 
biophysical models. They are semi-empirical and incorporate many 
statistically derived coefficients to parameterize crop growth and soil water 
retention properties. They assess the soil water content for commonly grown 
crops, for normal soil conditions and for readily available climatic or 
weather data. One such budget is described in technical detail here. 

The authors are indebted to many colleagues in the Land Resource Research 
Institute for assistance in preparing this bulletin. Review comments and 
editorial suggestions were provided by Johanne Boisvert, John Culley, Reinder 
De Jong, Henry Hayoe and Wolfgang Baier. In particular, suggestions by Johanne 
Boisvert led to several refinements in I/O features of the program. Helpful 
discussions and advice during the early stages of developing this version of 
the Versatile Budget came from Wilbur Sly, Stuart Edey and Wolfgang Baier. 
Contributions to background research were made by Lianne Dwyer and Wolfgang 
Baier, who co-authored the principle research papers which led to this 
version. Others, such as John Keng, Bob Stewart and Ken Wilkie (Technical 
University of Nova Scotia) also contributed useful discussions. The 
assistance given by the Data Processing staff of Agrometeorology is also much 
appreciated. 



3 - 



INTRODUCTION 



Soil moisture budgets, such as the Versatile Soil Moisture Budget (Baier 
and Robertson, 1966), integrate present and past weather events to simulate 
daily soil water contents. The advantage of making such weather based 
estimates, over taking field measurements, is that such estimates are 
repeatable and non-destructive. Although field measurements are essential for 
development and verification of computer models, estimates are possible at 
times and locations where measurements are not feasible. Model estimates can 
therefore extrapolate information gained from periodic validation 
measurements. A detailed discussion of tne range of soil water models that 
nave oeen developed was prepared by De Jong (1981). 

Soil moisture budgets have found a wide range of applications. Soil 
moisture reserve estimates dre used in many crop growth and yield simulation 
models. One such model was developed for forages (Selirio and Brown, 1978). 
Soil moisture reserves, oased on current and recent weather conditions, have 
been estimated in tiie prairie provinces and distributed weekly throughout the 
growing season (Edey, 1980). Moisture budgets have also been the basis of 
many field workday analyses (Dyer et al . , 1978; Dyer, 1980), which play an 
important role in planning farm field operations. 

Tne earliest budgets consisted of simple water balance equations, such as 
developed by Thorn thwaite and Mather (1955). With the improvements in 
electronic data processing came the concept of multi-layer soil moisture 
budgets. A multi-layer budget developed by Holmes and Robertson (1959), known 
as tne Modulated Budget, became one of the earliest and best known examples. 

Tne Versatile Soil Moisture Budget (VB) developed by Baier and Robertson 
(1966) was an improvement on the Modulated Budget and achieved wide use and 
popularity. The VB has been described in detail in two previous technical 
publications (Baier et al . , 1972; Baier et al . , 1979). Each of these included 
refinements and the addition of submodels to the previous versions. The 
budget described here is the third version of the Versatile Soil Moisture 
Budget to be described in technical detail and is referred to as Versatile 
Budget - Version Three (VBIII). 

This Budget (VBIII) includes features designed to make it more flexible 
and applicable to a wider range of uses. These new features include: 

Drying curve index which can generate a wide range of different types 
of drying patterns. Tne index eliminates the so-called "z-tables" 
being read in. 

Flexible soil zones. This budget can be run with two to six zones 
(or soil layers). As an auxiliary program, a routine is included 
whicn allows root coefficients to be modified to fit different zone 
patterns. Tnis program (listed in Appendix B) can also be applied to 
the water holding properties of the zones. 

Improved infiltration submodel. An experimental function for 
budgeting excess soil water is made available. 



- 4 - 



Reduced amount of data required as input controls (see Appendix C). 

Revised output format. Zonal soil moisture contents can be expressed 
on a volumetric percent basis and displayed graphically by a line 
printer plotting function. 

A variety of restart procedures are now available, recognizing a wide 
range of different possible applications to historical weather data. 

Tne Dasic structure of the budget can still be described by the flow chart 
presented by Baier et al . (1979) shown in Figure 1. Model components can be 
split into evaporation functions, including all crop and soil water extraction 
characteristics, and recharge functions including infiltration, drainage, 
runoff and snowpack submodels. The computational steps in both soil water 
extraction and recharge are the same as the previous VB (Baier et al . , 1979). 
However, many of the submodel components have had significant alterations. 
The input, computation and output of the new budget are shown in a flowchart 
in Appendix C. 

EVAPOTRANSPIRATION 



Tne basic drying relationsnip used by Baier and Robertson (1966) is still 
used nere. Tnis expresses the actual daily evapotranspiration (AE) as a 
function of the potential daily evapotranspiration rate (PE) as follows: 

n 
AE = Z kii.ij.Zi.PE (1) 

j=l Cj 

where AE = actual evapotranspiration for any day. 
n 

I = summation carried out from zone j = 1 to zone j = n. 
j=l 

kij= coefficient accounting for soil and plant characteristics 
in the jtn zone during growth stage i. 

Sj= available soil water in the jth zone at the morning 
Observation of each day. 

Cj= capacity for available water in the jth zone. 

Zj= adjustment factor for different types of soil dryness curves. 

PE = potential evapotranspiration on each day. 

By this equation water is withdrawn simultaneously from all depths and the 
daily decrease in soil moisture in each zone due to drying is computed. The 
feature which most distinguishes this series of budget models from other types 
of soil water models is the k and Z coefficients developed empirically for 
different root extraction and soil water retention patterns respectively. 
This relationship is used here, but some modifications were made so that the 
selection and input of k and Z are easier. 



- 5 - 




Figure 1 . Soil-root-atmosphere pathways for water in the Versatile Budget 



Digitized by the Internet Archive 
in 2013 



http://archive.org/details/versatilesoilmoi19841dyer 



- 7 - 



Reliable estimates of daily PE are required input to this model. For most 
sites in Canada with historical weather records these estimates have been made 
from standard meteorological data and are available for use in this model. 
General equations, based on regression techniques, have been devised for 
estimating PE from various combinations of available meteorological data 
(Baier and Robertson, 1965); however, any valid technique for estimating PE 
can be used as input to VB. 

Root Extraction 

Each k-coeff icient in eq. 1 reflects the root activity at different depths 
(zones) during different times of the growing season. The transition dates 
between crop stages must be read into the program for each year. The 
k-coeff icients in use at present have been determined by iterative comparisons 
between computed and measured soil moisture, or were estimated so that 
extraction rates resemble the most probable crop rooting pattern under the 
prevailing environmental conditions. 

The k-coeff icients are selected from a table which must be supplied as 
part of the control inputs. The k-coefficient table is based on 5 stages of 
growth and up to 6 soil zones (or layers). In previous versions these 
coefficients could be used only for a specific zoning pattern. That pattern 
required that 5, 7.5, 12.5, 25, 25, and 25% of the total plant available water 
capacity be attributed to zones one to six respectively. 

Recently it has been found that k-coeff icients can be adapted to soil 
zoning patterns other than the standard set used in the original budget (Dyer 
and Baier, 1980). In the same study it was found that changes in the number 
and distribution of soil zones has little effect on the performance of the 
budget. Therefore, the VBIII program allows between 2 and 6 zones to be 
used. Appendix E gives sets of k which were developed in previous studies for 
different crop stages for the specific zone pattern given above. To adapt 
these tables of k-coeff icients to other zoning patterns the user should 
consult Dyer and Baier (1980) or Appendix B which gives the computer program 
for making these conversions. The program can also convert zonal 
distributions of other control parameters, such as water holding properties. 

Dyer and Baier (1980) suggested that observed root patterns could be a 
basis for development of new sets of k-coeff icients for different crops. A 
comparison of the VBIII with corn (maize) in a growth chamber was made by Dyer 
and Dwyer (1982). This study demonstrated the use of observed root 
distributions for deriving sets of k-coeff icients which are applicable to a 
specific crop. Each kj was related to the fraction of total root mass in 
each zone (j). Observed root distributions were also used to determine plant 
water uptake from each soil zone by De Jong and Shaykewich (1981). The growth 
chamber experiment (Dyer and Dwyer, 1980) also showed that coefficients 
representing root distributions from well watered conditions could also be 
adapted to dry conditions by using the adjustment procedure described in the 
next section. 



- 8 - 



Crop Coefficients Adjustment 

In comparisons between observed and estimated soil moisture under 
non-irrigated crops, Baier (1969) found that during drought, plant roots 
absorbed comparatively more water from the lower, relatively moist layers than 
they did when the soil profile was uniformly moist. The effect of the 
k-coeff icients for the upper zones, where water is no longer or less readily 
available, is decreased by giving more influence to the lower zones where 
water is still available. Thus the k-coeff icient in each of the lower zones 
is increased as a function of the moisture content in the respective upper 
zones as follows: 

kj = kj + kj \ kjl - Sal (2) 

m=l ^m 

where kj= adjusted k-coeff icient for the jth zone (j=2,n) 

S^= available soil water in the mth zone 

Cpi= capacity for available water in the mth zone 
The use of this adjustment and the date of its commencement are optional. 

The sum of k-coeff icients over depth (all zones) is greater than one for 
periods of the growing season with fully developed crop canopies. These sums 
change with crop stages. This reflects the chafiging consumptive use factor 
defined by Baier and Russelo (1968), as the ratio of water use by a well 
watered crop to a free water surface. Consumptive use factors account for 
increased response to PE as the crop canopy develops and leaf area index 
values increase. Under well watered conditions, in a fully developed canopy 
AE can exceed PE. 

Soil Water Retention 

The evaporative loss from each zone was related to water content and soil 
water retention characteristics of that zone. In the previous VB each soil 
type was characterized by a drying curve, incorporated into equation 1 by the 
Z coefficient. For the water balance of a single soil layer, Z can be 
expressed as the ratio of the relative daily evapotranspiration (AE/PE) to the 
relative available water content (S/C). At each specific relative available 
water content a new value of Z is required. The curves developed and used by 
Baier et al. (1979) to describe the relationships between AE/PE and S/C are 
shown in Figure 2. 



- 9 - 




100 



Figure 2 



-r 

90 80 70 60 50 40 30 20 
AVAILABLE SOIL MOISTURE (%) 

Proposals for the relationships between 
Ml-.W: ratio and available soil moisture (S/C) 



Previously, sets of Z were read into the budget in tabular form, including 
one value for each of 100 possible S/C values. The so-called "z-tables" that 
correspond to curves in Figure 2, are listed in Appendix E. Each z-table is 
made up of Z values which correspond to 100 possible S/C values from to 1. 
The z-tables had several disadvantages including a significant increase in the 
required control data and a restriction on the drying curves available. 
Therefore, an index for generalizing drying curves was developed. Derivation 
of this index has been shown elsewhere (Dyer and Baier, 1979a). 

Typical drying curves, such as those in Figure 2, have the general 
characteristic of allowing evapotranspiration at the potential rate until some 
value of S/C, then having AE/PE decrease as a function of S/C. The decreasing 
portion of the curves can be described as concave upward, linear or convex 
downward. The general form of the index can generate all three classes of 
curves. The equation for the index is as follows: 



Z = 



hmnp m 
(1) [*f 



(¥) f] 



(3) 



where X = S/C 
and X ^R ^ 1 



- 10 - 



Table 1. COMBINATION OF CONTROL PARAMETERS USED IN 
EQUATION 3 TO GENERATE DIFFERENT DRYING 
CURVES 



Curves' shape 



m n h 



Curve number from 
Fig. 3a, b 



Concave 

Convex 

Deeply convexed 

Linear 

Potential 



1 1 
1 1 1 



(1) 
(3) 



1 1 2,3 or 4 (4, 5 and 6) 



1 



1 



(2) 




ASM ASMC IM 



rigure 7>: Drying curves derived from eq 7> for (a) R= 0.8 
and (b) R= O.S and a function for h at X= 0.5 R. 



- 11 - 



To use the index the parameters m, n, h and R must be defined. The type 
of curve is controlled by m and n, each of which is either or 1 . The degree 
of dovmward curvature is controlled by h. The value of X where 
evapotranspiration changes from potential to a function of S/C, is equal to R. 
Examples of curves which can be generated are shown in Figure 3 for two values 
of R and five values of h (0 to 4). Table 1 gives the h, m and n which 
generate each type of curve. 

The index can be more flexible by making the curvature parameter (h) 
non-integer, h can be defined as a logarithmic function of Y, X and R (where 
Y = AE/PE). The right hand axes of Figure 3 show the unique relationship 
between h and Y at X =0 .5R. From these axes, and selected "halfway" values of 
X in the range corresponding to decreasing Y, estimates of h can be made. 
Thus approximate "curve fitting" of the index to an appropriate curve for a 
particular type of soil can be done. Curves similar to those shown in Figure 
2 can be generated. When the index is used new z-tables are actually 
generated internally. 

Along with this new index VBIII can still accomodate the z-tables used in 
the previous versions. Generally curves which resemble D or G in Figure 2 are 
best applied to high clay content soils, whereas curves such as E and H would 
be applied to medium textured soils and F to yery coarse textured soils. 

Alternate Drying Curves 

Two z-tables can be read and used by the program at one time. This 
feature can be used in two ways: (1) in heterogeneous soils having two 
distinct textural horizons, one z-table can be applied to the upper zones and 
another to the lower zones. If a homogeneous soil is assumed, one z-table is 
used throughout all six zones. (2) The relationships in Figure 2 depend on 
whether an active root system is present, or the soil is fallow. Therefore, 
to simulate the soil moisture distribution in a crop-fallow rotation, one 
drying curve (or z-table) can be used in cropped years and another drying 
curve (or z-table) can be used in fallow years. The procedure for using these 
two options is described in Appendix C. 



INFILTRATION AND DRAINAGE 



A new submodel for infiltration and drainage is included in VBIII to allow 
the budget to be applied more effectively to excess water problems. The 
submodel is based on a simple two zone budget developed for estimating fall 
field work conditions (Dyer and Baier, 1979). The basic concept is that 
drainage of excess, or gravity water is not immediate but takes place over one 
or more days. An essential assumption is that each zone can be filled to its 
saturation content, rather than just to field capacity. The excess or gravity 
water, that is the range of water content between field capacity and 
saturation, is free to drain out of each zone. However, only a certain 
fraction is allowed to drain from a near-surface zone on any one day. 



- 12 - 



To simulate the persistance of excess water near the surface from day to 
day the sequence of drainage into or out of each zone is critical. In the 
two-zone budget (Dyer and Baier, 1979b) drainage out of both zones was done 
before rainfall or surface water was added to the top zone, causing a one-day 
delay in drainage out of zone 1. When more than two zones are used this same 
sequence is followed but the computations become more complex. The one day 
delay principle is maintained, by splitting the total number of zones into two 
drainage layers. The first drainage layer defines or includes the maximum 
depth that surface water can infiltrate in one day. 




Figure 4. Assumed infiltration pathway for rain water through 
four layers of soi 1 . 



- 13 - 



The computation steps in this submodel are illustrated in Figure 4 for a 
four-zone budget. These steps are: (1) drainage out of the bottom drainage 
layer, (2) drainage out of the upper drainage layer into the second drainage 
layer, (3) addition of precipitation to surface water and subtraction of 
surface runoff, and (4) drainage of surface water into the first drainage 
layer. This computational sequence ensures that surface water cannot enter or 
penetrate through the second drainage layer in one day. 

Within each drainage layer the drainage water from above is added in three 
steps. First, excess water in any zone in the drainage layer is assumed 
capable of draining to the next drainage layer. Second, the plant available 
water deficits (below field capacity) are satisfied in sequence downward from 
the sum of excess water draining from above. Third, when there still remains 
excess water (more drainage water than required to satisfy the plant available 
water deficits in all zones in the layer) then this water is distributed 
evenly over the excess void spaces (field capacity to saturation) in all zones 
in the layer. 

Drainage Rate Parameters 

To operate the submodel and characterize drainage patterns of various 
types of soils several new control coefficients are required in VBIII. These 
include the saturation level water contents (or total void spaces per zone in 
mm), the number of zones in the upper drainage layer, a drainage rate 
coefficient and maximum amounts of drainage. The drainage rate defines the 
fraction of excess water in Drainage Layer 1 which can drain into Drainage 
Layer 2 during each day. This ensures that a small amount of excess water 
persists in Drainage Layer 1 until the next day. 

Three coefficients control the maximum amount of water which can drain 
past three points in the profile (or levels) each day. These are the maximum 
water volumes which can infiltrate the surface (Drainage Layer 1), which can 
enter Drainage Layer 2 and which can drain out of the soil profile. These 
coefficients are in millimeters of water per day and can be considered as 
maximum daily water conductivities. 

Tne depth of Drainage Layer 1 defines the maximum wetting front 
penetration from the surface in one day. Because the submodel is sensitive to 
the range between field capacity and saturation, the permanent wilting point 
of each zone is also required to distinguish field capacity from plant 
available water holding capacity. Soils can have similar plant available 
water holding capacities, but different field capacities, due to different 
permanent wilting points. This difference affects drainage patterns. 

Although this submodel has not been tested against soil moisture 
observations, the two-zone version has proven successful in making estimates 
of days with surface soil conditons too wet to permit field work (Dyer and 
Baier, 1979b). The performance of the function was also assessed in a set of 
sensitivity tests not shown here, but which revealed that fine textured soils 
(higher clay and silt fractions), which are generally less permeable, are best 
simulated by shallow one-day drainage depths (20 cms) and low drainage 
coefficients (approximately 50%). For coarser textured soils one day drainage 



- 14 - 



depths of 40 to 60 cm are more realistic (assuming 120 cm total depth) and a 
drainage coefficient of 80 or 90%. The original two-zone application of this 
function (Dyer and Baier, 1979b) used 80%. The function is also sensitive to 
the range between field capacity and saturation, particularly for relatively 
high field capacity. Users should be aware that this new submodel is still 
experimental . 

SNOW BUDGET 



In some applications in temperate climates it is necessary to calculate a 
daily soil moisture content throughout the year, particularly when a 
reasonable estimate in spring is required as a starting point for the water 
budgeting during the growing season. In climates where snow occurs, the 
computation of soil moisture requires the amount of water penetrating the soil 
from snow. Therefore, a simple snow budget was developed in the previous 
version (Baier et al . , 1979). The snow budget used here is little different 
from the previous version, but control parameters have been modified. 

The inputs required for the snow budget are the daily maximum temperature 
and the precipitation total. Because the form of precipitation is not defined 
in the daily weather data used here, part of the snow submodel is dedicated to 
determining the precipitation form. Snow is not assumed unless the smoothed 
daily maximum temperature (Amax) is below a selected threshold value. The 
5-day binomial smoothing function used in the previous budget was replaced by 
a 3-day function. 

AmaxT= (3Tmax-j+ 2TmaxT_-i+ Tmaxi_2)/6 (4) 

where i = day and Tmax = daily maximum temperature 

This change means that temperatures for the two days following the date of 
calculations are not required input, as they were in the previous version. 

In fall and early winter (July 1 to December 31), precipitation is assumed 
to be in the form of snow if Amax < threshold 1. Snow is then accumulated on 
the soil surface until Amax t threshold 1, when the melted snow is taken into 
the soil. In late winter and spring (January 1 to June 30) precipitation is 
assumed to occur as snow if Amax < threshold 2 and it is then accumulated on 
the surface. Temperature thresholds for Canada were determined by using 
various thresholds in repeated computer runs to compare estimated with 
observed snowfall. It was found that precipitation can be expected to occur 
as snow when the daily maximum temperature is below the thresholds in Table 2. 
It was also found in developing this submodel, that snow was more likely in 
colder, less humid climates in the same ambient temperature range. This is 
reflected by the higher thresholds for colder climates in Table 2. 



- 15 - 



TABLE 2: MAXIMUM TEMPERATURE THRESHOLDS FOR SNOW 



Region Threshold 1 Threshold 2 . 

July - Dec Jario - June 

COLD 

Central B.C., 5"c 7^C 

Prairies 

WARM 

Southern B.Cc, 1°C 2°C 

Ontario, 

Quebec, 

Atlantic Provinces 



The incidence of snowfall with the higher surface temperatures in spring 
reflects the seasonal difference in lapse rates. In spring, when the ratio of 
incoming solar to outgoing terrestrial radiation is usually higher than in 
fall, the lapse rates are steeper and conditions are less stable. Thus the 
chances of precipitation reaching the surface as snow rather than as rain are 
increased. This is reflected by using higher temperature thresholds for 
spring which designates precipitation to be snow more often. In Western 
Canada threshold values are higher because a larger proportion of 
precipitation occurs as snow, due to relatively more surface heating and less 
stable air masses. 

Snowfall is also restricted to a period between specified dates in fall 
and spring. These two dates are required input control parameters. 



- 16 - 



Snowpack Losses 

Only a fraction of the measured snowfall actually enters the soil after 
snowinelt, because a large proportion of the snow blows away, evaporates, or 
when melted, runs off as surface-water. The snowpack is also decreased daily 
by an amount equal to PE. To describe the snow remaining after blow-off, a 
snow coefficient was defined. The two most commonly used snow coefficients 
are 0.7 for stubble-covered fields and 0.5 for fallow fields. These values, 
which indicate the fraction of snow remaining after blow-off, were developed 
in repeated computer runs in which the snow coefficients were changed until 
the estimates were closest to the available soil moisture measurements in 
spring. In areas where blowing snow is not as serious a problem higher values 
can be used. 

Provision is made for an alternate snow coefficient to be used if soil 
surface conditons change from one year to another. This situation occurs in a 
crop fallow system that has stubble during the first winter and is bare during 
the second winter. If the crop development stage on July 1 is past the 
preemergence-f allow stage, then a crop is assumed during that year and the 
first snow coefficient is used. Otherwise a fallow state is assumed and the 
second coefficient is used. July 1 was selected as the middle of the growing 
season for the northern hemisphere. In VBIII July 1 can be replaced with a 
date selected by the user. This feature is designed to allow a change in the 
snow accumulation rate in midwinter. 

The choice of coefficients usually depends on the ground cover during 
winter and on the growth stage. When growth (or a ground cover) is assumed, 
the first coefficient is used. In some cases, such as a perennial crop, there 
may be no growth but a ground cover could still be present in winter; thus 
both coefficients would be 0.7. In cases where fall tillage is practiced 
there would be summer growth but no ground cover in winter, and both 
coefficients would be 0.5. Even though this system was first developed for 
wheat-fallow rotation it can be adapted to many cropping practices. 

Snowmelt Infiltration 

The factors considered for the contribution of snow to soil moisture are 
the rate of snowmelt, the retention of meltwater within the snowpack and the 
rate of penetration of meltwater into frozen or partially thawed soil. The 
rate at which snow melts is correlated with both the air temperature and the 
time of year. A set of four snoivmelt curves was defined (McKay 1964) for four 
temperature intervals; 0-2.8, 2.8-5.6, 5.6-8.3, and above 8.3(OC). 

Previously, empirical constants for each month and for each of the four 
temperature intervals were required input control data to calculate daily snow 
melt. In VBIII, temperature dependent, monthly snowmelt values were 
calculated externally and stored as internal data. So the McKay equation 
(1964) used in the previous version does not appear in the VBIII program. 
Early and late in each month melt rates are averaged with the previous and the 
following months, respectively. This smoothing was required since daily snow 
melt values are no longer calculated or stored. 



- 17 - 



The melt term has the restriction that it cannot exceed the total moisture 
available in the snowpack. The snowpack is considered capable of retaining 
15% of its volume as meltwater. The amount of melt must exceed this threshold 
before any moisture is presumed to reach the soil. Whenever the maximum daily 
temperature is less than 0^0, the portion of melt retained in the pack is 
considered once again to be part of the snowpack. All melt which reaches the 
soil is considered to enter it by the normal infiltration process. The first 
day after the designated date in fall that Amax < the threshold is considered 
to be the beginning of the winter budget. If the maximum temperature is less 
than -6.70c, the previous snowmelt is considered frozen in the upper layers 
of the soil, thus affecting penetration and runoff of future snowmelt. In 
VBIII the reduced rate of meltwater infiltration is related to the fraction of 
unfilled excess void space in the top (one-day) drainage depth. The drainage 
rate is assumed to be retarded by freezing. This is simulated in VBIII by 
reducing the drainage rate coefficient. 



FIELD PERFORMANCE APPRAISAL 



The performance of VBIII was assessed against soil moisture observations 
taken under alfalfa on a number of soils over three years (1966 to 1968) in 
South Western Ontario. These data were compiled by Selirio et al. (1978) at 
the University of Quel ph. Three sites were selected, representing three 
different soil textures. The model was run with data from nearby weather 
sites. These sites were Delhi, Woodstock and Kolher representing Fox Loamy 
Sand, Embro silt Loam and Haldimand Clay, respectively. The model was 
initialized on the day of the first observation with starting soil moisture 
contents equal the first set of observations. The zones used in the budget 
were equal to the six observation layers. The root (k) coefficients given by 
Baier et al . (1979) for alfalfa (Appendix E) were adapted to the zones used 
here by the technique described by Dyer and Baier (1980). 

The maximum possible water content (mm) or saturation level (Sat) in each 
zone was calculated from measured bulk density (bd) and grain density (gd) as 
follows: 

Sat = gd - bd X (zone thickness) (5) 

Field capacity and the permanent wilting point for each zone were based on 
measurements taken at the sites. Field capacities, permanent wilting points 
and bulk densities, along with soil texture and daily moisture observations, 
were presented by Selirio et al . (1978). Permanent wilting points, field 
capacities and saturation levels used here are presented in Table 3. The 
parameters used in equation 3 to generate drying curves are also given in 
Table 3. Water holding properties and k-coefficients were converted to depth 
of observation basis by the program in Appendix B. 



- 18 



TABLE 3: Water holding properties used to compare VBIII estimates to 
observations of soil moisture contents from three sites 



Zones 


1 


2 


3 


4 


5 


6 


Depth (cm) 


15.0 


30.0 


46.0 


61.0 


91.0 


152.0 


Permanent Wilting Point (mm) 












Fox Loamy Sand 


5.1 


6.6 


4.4 


6.8 


13.9 


19.7 


Embro Silt Loam 


11.0 


12.0 


13.0 


14.0 


40.0 


60.0 


Haldimand Clay 


12.5 


19.5 


21.0 


21.5 


45.0 


88.7 


Field Capacity (mm) 














Fox Loamy Sand 


12.7 


16.4 


9.7 


12.7 


26.1 


50.4 


Embro Silt Loam 


42.0 


35.9 


42.2 


44.3 


97.4 


175.8 


Haldimand Clay 


42.4 


44.5 


47.1 


45.0 


93.0 


180.5 


Saturation (mm) 














Fox Loamy Sand 


41.8 


54.1 


37.2 


90.3 


194.3 


348.1 


Embro Silt Loam 


59.4 


65.0 


63.4 


65.2 


100.6 


343.4 


Haldimand Clay 


90.0 


76.3 


82.0 


70.2 


139.5 


259.4 



Drying Curve (z) Parameters 





m 


11 


h 


R 


Fox Loamy Sand 


1 


1 


1.0 


.5 2 


Embro Silt Loam 


1 


1 


0.2 


.7 


Haldimand Clay 


1 


1 


1.0 


.6 



To the bottom of each zone 

2 
Changed to .6 in zone 6 



- 19 - 



RESULTS 



The comparisons shown in Figures 5 to 7, give estimates of soil moisture 
content vs observations taken as close as possible to the end of each growth 
stage. Only the top three soil zones are shown, representing a depth of 45 
cm since there was very little variation below the third zone. These 
comparisons are considered as only a demonstration because the accuracy of 
VBIII was hampered by a number of limitations in the observations used. 
Therefore, only a limited number of comparisons are shown and no statistical 
tests were made. They are presented here so that users can appreciate the 
performance to be expected of VBIII. 




OBSERVED 



Figure 5. ComDarisoii of estimated and observed soil moisture contents (% 
voliimetric) in the top 15 cm at three sites. 



- 20 - 



The primary limitation was the use of weather data from weather sites 
which were several km away from the soil moisture observation sites. Both 
the levels and the occurrence of rain were known to be quite different, since 
Selirio et al. (1978) did present some rainfall records at each site. These 
differences largely account for the scatter of comparisons in the top zones 
(Figure 5) . 



40 I- 



35 - 



30 =_ 



25 



20 



UJ 



15 



ZONE 2 



FIELD CAPACITY 

(HALDIMANDCLAY) 



(EMBRO SILT LOAM) 




(FOX LOAMY SAND) 



1 HALDIMAND CLAY ▲ 
EMBRO SILT LOAM • 
FOX LOAMY SAND ■ 



J I I I I I I I I I I I \ \ I I I I I I I I I I I I I I I I I I I I I 

10 15 20 25 30 35 40 45 

OBSERVED 



Figure 6. Comparison of estimated and observed soil moisture contents 
(°6 volumetric) in 16 to 30 cm layer of soil at three sites. 



- 21 - 



The VBIII estimates were also restricted by the use of measured soil water 
retention characteristics, listed in Table 3, which appear to be incompatible 
with the soil water content observations. For instance, the range of observed 
water contents is consistently higher than the observed range from permanent 
wilting point to field capacity even during periods without heavy rain. Most 
estimates fall well within the field capacity to permanent wilting point 
range. This caused the comparisons to be below the one to one line in all 
three zones (Figures 5 to 7). 



40r 




Fioiire 7: Cumparison of estimated and observed soil moisrure contents 
[% volumetric) in 31 to 46 cm layer of soil at three sites. 



- 22 " 



In spite of these shortcomings, there does appear to be reasonable 
correspondence between observations and estimates. The differences due to 
soil type are realistic. The rate of decrease in estimated water contents 
with time is also similar to observed decreases. The best fit appears to be 
in zone 3 in the two finer textured soils. 

In the previous version of the VB (Baier et al., 1979) the results of a 
number of field validation trials showed that the VB performs acceptably well 
under semi-arid climates in water deficit conditions. It was not felt to be 
necessary to present these results again here. 

SUMMARY 

The soil moisture budgeting concept has been an important part of 
modelling and predicting soil water use by plants. The popularity of this 
concept is increasing. Soil water and crop yield predicting schemes, based on 
budgets are becoming more widely accepted and used. As the number of 
applications increase, the need for improvements in flexibility and 
applicability will also increase. A good example is the application to excess 
water problems. Restricting drainage rates in the lower zones to simulate 
water table effects, such as done by De Jong and Shaykewich (1981), could be a 
valuable expansion of the excess near-surface water submodel described here. 
There is a broader range of crops that require estimates of soil water use 
than when the original VB was designed. A soil surface evaporation term which 
is separate from root extraction, such as was suggested by Dyer and Dwyer 
(1982), could enhance the derivation of new root coefficients. There is also 
a need for budgets which are simpler. Often fewer soil zones are required and 
one or more sub-models, such as the snow-budget, can be ignored. 

It is felt that the version of the Versatile Budget described here will 
provide much of the flexibility needed in some of the new applications. These 
applications can provide additional opportunities for comparison with field 
observations. In making the technical detail of VBIII available, the use and 
appraisal by other researchers is hoped for. 



- 23 - 



REFERENCES 



Baier, W. 1969. Observed and estimated seasonal soil water variations under 
nonirrigated sod. Can J. Soil Sci. 49:181-188. 

Baier, W., and G.W. Robertson. 1966. A new versatile soil moisture budget. 
Can J. Plant Sci. 46:299-315. 

Baier, W. and G.W. Robertson, 1965. Estimation of latent evaporation from 
simple weather observations. Can J. Plant Sci. 45:276-284. 

Baier, W., and D.A. Russelo. 1968. A computer program for estimating 
risks of irrigation requirements from climatic data. Tech. Bull. 59, 
Agrometeorol. Sect., Plant Res. Inst., Agric. Can., Ottawa, 48pp. 

Baier, W. , D.Z. Chaput, D.A. Russelo, and W.R. Sharp, 1972. Soil moisture 
estimator program system. Tech. Bull. 78, Agrometeorology Section, 
Research Branch, Agriculture Canada, Ottawa, Ont. 35pp. 

Baier, W., J. A. Dyer, and W.R. Sharp. 1979. The versatile soil moisture 
budget. Tech. Bull. 87, Agrometeorology Section, Research Branch, 
Agriculture Canada, Ottawa, Ont. 52pp. 

De Jong, R. and C.F. Shaykewich. 1981. A soil water budget model with a 
nearly impermeable layer. Can. J. Soil Sci. 61:361-371. 

De Jong, R. 1981. Soil water models a review. Land Resource Research 
Institute. Agriculture Canada. Contribution No. 123. 39pp. 

Dyer, J. A. 1980. Fall field workdays in Canada. Tech. Bull. 92, 

Agrometeorology Section, Research Branch, Agriculture Canada, Ottawa, 
Ontario. 59pp. 

Dyer, J. A., and W. Baier, 1980. The influence of zones in budgeting plant 
available soil moisture. Can. Agric. Eng. 22:65-70. 

Dyer, J. A., and W. Baier. 1979a. An index for soil moisture drying 
patterns. Can. Agric. Eng. 21:117-118. 

Dyer, J. A., and W. Baier. 1979b. Weather-based estimation of field 
workdays in fall. Can. Agric. Eng. 21:119-222. 

Dyer, J. A., and L.M. Dwyer. 1982. Root extraction coefficients for soil 
moisture budgeting derived from measured root densities. Can. Agric. 
Eng. 24(2):81-86. 

Dyer, J. A., W. Baier, H.N. Hayhoe, and G. Fisher. 1978. Spring field 
workday probabilities for selected sites across Canada. Tech. Bull. 
86. Agrometeorology Section, Research Branch, Agriculture Canada, 
Ottawa, Ont. 93pp. 



- 24 



Edey, S.N. 1980. SMEP - an approach to areal assessment of soil moisture 
reserves in the prairie provinces. Canada Agriculture. 25(2):26-29. 

Holmes, R.M., and G.W. Robertson. 1959. A modulated soil moisture budget. 
Mon. Weather Rev. 87:101-106. 

McKay, G.A. 1964. Relationships between snow survey and climatological 
measurements for the Canadian Great Plains: prepared for the Western 
Snow Conference, Nelson, B.C. 19pp. 

Selirio, I.S. and D.M. Brown. 1979. Soil moisture-based simulation of 
forage yield. Agric. Meteorology. 20:99-114. 

Selirio, I.S., D.M. Brown and K.M. King. 1978. Soil moisture observations 
in Southern Ontario, 1966-1968. Univ. of Guelph, Ontario Agricultural 
College, Dept. Land Resource Sci., Tech. Memo. 78-2. 44pp. 

Thornthwaite, C.W., and J.R. Mather. 1955. The water balance. Climtology 
Vllld): Drexel Inst, of Technol . , N.J. 104pp. 



A-1 

APPENDIX A 
VB III Program 



The following is a listing of the computer program for VBIII. This 
program is a revision of the previous Versatile Budget program and is not a 
new program. As a result its design, structure and many of its variable names 
are unchanged. Also unchanged is the way in which the program is set up to 
read and write metric units, A flowchart (Figure A-1) is provided to assist 
the user In interpreting the computational sequence and restart options of 
this version. 

Variable names used in the main text to discuss the principle algorithms 
are not the same as those used in the Fortran program. For discussion 
purposes simple, one-character variable names are generally more convenient 
and easy to follow than Fortran code names, which often use four or more 
characters. Whenever possible, variable names in the text were chosen to be 
consistent with the most relevant references to previous work. The following 
table provides a conversion of variable names from the main text to the 
program code names. 

The authors are aware that this program itself is in need of 
streamlining. The intent of providing this version is to make available an 
experimental version of VB, so that other researchers would be able to use and 
assess the most recent additions. Input and output still require considerable 
effort by the user. But I/O formats, remain as much as possible, similar to 
the I/O format of the previous version (Baier et al., 1979). More details on 
input and output format are given in Appendices C and D, respectively. 



TABLE A-1. The program variable names which correspond 
to symbols and terms used in the main text 



SYMBOLS AND TERMS 



VARIABLE NAMES 



AE 

PE 
k 

Z 
S 
C 

J 

m 

n 

h 

R 

X 

Amax 

Tmax 

SNOUMELT 

RAIN 

RUNOFF 

SURFACE WATER 

GROUND WATER 

Deficit 



SUMDEL 

PE OPE 

CDF, COEFS 

WORK, TABLE 1, 2 

CONTNT 

CAPAC 

I 

M9 

N9 

H9 

R9 

X9, DEL 

MAXSM 

MAX, PAX 

DO, AQ 

PRECIP, PCP 

RUNOFF 

PDL 

AYE, EXTD 

X 



A-2 



CLstart V 



^Read snow, soil and root controls 



4. 



I 



Read vears of run and 
crop stage controls 




—y Read daily weather records 




^Yes 

{ Snow budget submodel 



Evapotranspiration submodel 



T 



Infiltration/Drainage Submodel 



Apply extraction and infiltration 
calculations to each zone 




FIGURE A-1. VBIII PROGRAN FLOWCHART. 



A- 3 



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B-1 

APPENDIX B 
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C-1 

APPENDIX C 
RUN CONTROL DATA 



The following section describes and lists (Table C-1) the input controls 
to VBIII. The definition of the input variable names are given in comment 
statements within the program. Table A-1 allows the user to relate the 
controls back to the main text. They are listed here according to the 
particular input record that each appears on and the position in that record. 
Each record is a punch card, or a card image, and is 72 characters in length. 
Table C-1 lists the input parameters according to a numbered set of controls, 
each of which consists of one or more records. Each input record is also 
specified. Detailed instructions for each input variable are listed in Table 
C-2. Sample input control files are provided in Table C-3 for two different 
soil textures. The following description is intended for users who have some 
familiarity with computer programming in Fortran, but the user does not 
necessarily need to be proficient. 

Numbers in brackets following some variables define the dimensions of the 
non-scalar variables. Single numbers give the length, or total possible 
number of entries for vectors. For example CAPAC(6) stores field capacity for 
zones 1 to 6. The variables followed by two numbers within brackets, 
separated by a comma, are matrices. For example in C0EFS(5,6) there are 5 rows 
(one for each possible growth stage) and 6 columns (up to 6 zones). Each row 
is a new card (or card image). 

The control parameters fall into two categories depending on whether they 
are integer or non-integer (real) numbers. Following the standard 
conventions, variable names starting with any letter I through N are 
integers. All other variables are real numbers, except for HEAD, which is a 
title. For real number inputs the location of the decimal points is specified. 

The formats for all input control records are also shown in Table C-1. 
The letters in format statements, I,F and A designate input variables as 
integer, real and alphanumeric (title card) respectively. The number to the 
left of each letter indicates the number of variables read by each format, the 
number to the right indicates the length of record required by each variable 
(or element in the variable) and for real numbers, the number following the 
decimal indicates the number of places after the decimal. Blank spaces are 
indicated by Xs. 

Soil Description 

To describe a particular soil, the drying, drainage and water holding 
characteristics must be defined. The depth of soil and the zoning patterns 
must also be specified. The index for drying rates is enacted by defining two 
sets of parameters in Controls 3 and 5. These are M9, N9, H9, and R9 , based 
on parameters m,n, h and R in Table 1 of the main text. Records 4 and 6, 
which allow the z-tables to be read in, as in the previous version, are 
optional and can be omitted. These z-tables are given in Table E-2. Writing 
of z-tables is controlled by K in record 3 (not to be confused with 
k-coefficients described in the main text). 



C-2 



For K equal to any integer other than zero, z-tables are printed. A blank 
card for sets 3 and 5 causes the z-tables to be read-in requiring 10 cards 
each for controls and 4 and 6. Controls 3 and 5 cause z-tables to be 
generated internally. KNTROL defines the number of zones using the first 
z-table. For the option of using the second z-table to differentiate active 
root conditions from fallow conditions (see Alternate Drying Curves: Main 
text) KNTROL = 7. 

Soil Water Holding Properties 

Water holding properties include the saturation level (EXCAP), the field 
capacity (CAPAC) and the permanent wilting point (PRMWLT) of each zone. The 
number of zones (or soil layers) is defined by the number of elements in CAPAC 
(control 7). To describe a four-zone budget for example, supply values for 
the first four elements of this vector and leave the last two blank. If more 
than four values are provided in any parameters on controls 8 to 10 they will 
be ignored. If fewer than four values are provided zeroes will be substituted 
for the missing values. The actual depth (cm) to the bottom of each zone from 
the surface is defined in OBDPTH (control 10). These values are required to 
printout soil moisture contents on a percent volumetric basis or to plot 
them. The dimensions of the plot are controlled by SCL and IHOR which are 
described in Appendix D. 

The infiltration/drainage function is controlled by the three maximum 
daily drainage amounts (DRN(l-3)), the fractional coefficient for drainage out 
of drainage layer 1 (DRN(4)) and by the number of zones in the top drainage 
layer (IFRNT). Other important factors are the actual depth of drainage Layer 
1 and the differences between saturation and field capacity (EXCAP and CAPAC 
respectively). Default values for DRN and IFRNT are built into the program, 
so these can be left blank. TRP and SSLP are experimental parameters used to 
describe surface water and runoff rates and should be used with caution. TRP 
is the average depth of surface water trapped in depressions and not available 
for surface runoff. SSLP determines the rate of runoff. All 
infiltration/drainage parameters are supplied on Control number 9. 

Crop Growth and Root Characteristics 

The table of root coefficients is entered by zone and crop growth stage in 
COEFS (Control 11). When less than six zones are used, say four, then only 
four k-coefficients for each growth stage are required. The number of 
k-coefficients per stage should not affect the consumptive use factor assumed 
for each stage. A control card is required for each of the five growth 
stages, but these can be blank. Five are needed because Control 11 is always 
read in by five read steps or lines. 

The crop stage dates are entered by reading the array IDATES in Control 
13. Not all crop growth stage dates in the run are required as input unless 
they are all unique. If the dates are repeating from one year or group of 
years to the next then the dates specified in IDATES can be re-used. This 
feature can be used to reduce the required input for two-year (or more) 



C-3 

rotations. For example, when the number of stages read-in (ISTGES) is ]0 and 
the number per year (NSY) is 5 (control 12), a repeating two-year rotation is 
given when the total number of years is greater than 2. The span of years 
analysed is set by ISYR and NDYR (Control 12). When the starting (ISYR) and 
ending (NDYR) years are given, only the month/day is required for each stage 
date, rather than the year/month/day. A typical stage date of June 15, 1953 
would be read-in as "630615" or simply as "0615", if ISYR is specified. When 
stage dates are non-repeating throughout the run, then all dates can be 
entered in a string (12 dates per record), provided that the total number does 
not exceed 65 and is one more than the specified number of stages to be read 
(ISTGES). 

Snow Budget 

To enact this submodel, spring and fall termination and initiation dates 
and threshold temperatures are required. Two coefficients giving the fraction 
of daily snowfall added to the snowpack after blow-off are entered as SMCOF. 
The date for converting from the first to the second coefficient (KSNO) can be 
specified. A bypass (NOSNO) for the snow submodel is also available. All 
controls for this submodel are on control 2. 

Restart Options 

A feature of VBIII, which was developed to satisfy a variety of 
multi-weather file applications, is the flexibility in the restart 
procedures. The options include the ability to analyse (1) a complete weather 
file with only one set of controls being specified in the budget; (2) more 
than one segment of a weather file, skipping periods within a file, with only 
the crop stage dates and run dates (ISYR and NDYR) being re-specified; (3) 
multiple weather files with a new set of controls for each file; (4) multiple 
weather files with the original set of controls; (5) multiple weather files 
with the original controls, but the initial moisture contents being re-defined 
for each file; (6) multiple weather files with only the crop stage and 
start/end dates re-defined for each file; and (7) multiple weather files with 
both initial soil moisture and control dates being re-defined without changing 
other controls. These options are illustrated in the program flowchart in 
Figure A-1. 

The restart options are controlled by the variables NFLE and ISEN 
(controls 1 and 14). The soil moisture contents are re-initialized after the 
last specified stage date (i.e. end of rotation) by reading CONRST on control 
15. An integer value must be specified for MRSTl (on Control 8), otherwise 
soil moisture is re-initialized at the end of each rotation without 
re-defining other controls or skipping any weather records. NFLE and ISEN can 
be left blank on the first control record, but are re-entered at the end of 
the program. 



C-4 



The re-start control parameters are specified as follows: NFLE can be or 
1, ISEN can be 0, 1, 2 or 99 and MRSTI can be or 1. For NFLE = and ISEN = 
99 the program reads, but doesn't compute, to the weather file end, and then 
reads a new NFLE and ISEN. For NFLE = and ISEN = 1 the program 
re-initializes with the original starting moisture contents, whereas when ISEN 
= 2 a new set of starting moisture contents is read- in. When NFLE > the 
program reads a complete new set of controls and ISEN is over-ridden. 

Another option is to enter the control coefficients in two files. When 
record number 14 is not provided, the program looks for another record 14 if 
an end of file is encountered. The new record must be supplied in a new file. 
This feature is useful when the budget is applied to a number of sites or 
weather data files with one set of controls which may be altered or replaced. 



TABLE C-1. Control data required to run VBIII, specified by the sequence 
of records, variable names and the format of each record 



RECORD 
INPUT FORMAT FORMATS REPEATED 

(5I3,14A4, Al) 

(416, 6X, 2F10.5, 215) 

(215, 2F10.5, 215) 

{10F5.2) xlO 

(215, 2F10.5, 15) 

(10F5.2) xlO 

{6F5.1) 

(6F5.1, 5X, 2F5.1, 15, 

10X,2I5) 

(14F-5.1, 12) 

6F 5,1 ,5X, 6F5.1, F5.2, 12) 
(6F5.2) x5 

(214, 8X, 314) 

(1316) x(l to 5) 

(513, 14A4, Al) 
(6F5.1) 

(214, 8X, 314) 
(1216) x(l to 5) 



Note: that controls 4, 6, 14, 15, 16 and 17 are all optional and can be omitted. 
♦Leave DRN(5) and 0RN(6) blank 



NUMBER 


VARIABLE NAMES AND DIMENSIONS 


1 


NFLE, ISEN, NP, INT, IP. HEAD(57 characters) 


2 


ISFL, lESG. IFTP, ISTP, SMC0F(2), NOSNO, KSNO 


3 


M9, N9, H9, R9, K, KNTROL 


4 


TABLE 1(100) 


5 


M9, N9, H9, R9, K 


6 


TABLE 2(100) 


7 


CAPAC{6) 


8 


C0NTNT(6), SNSTR, PESTR. MRSTI, IDRN, NDRN 


9 


EXCAP(6), TRP, SSLP, DRN(6), IFRNT* 


10 


PRMWLT(6), 0BDPTH(6), SCL, IHOR 


11 


C0EFS(5,6) 


12 


ISTGES, NEW, ISYR, NDYR, NSY 


13 


IDATES(65) 


14 


NFLE, ISEN, NP, INT, IP, HEAD(57 characters) 


15 


C0NRST(6) 


16 


ISTGES, NEW, ISYR, NDYR, NSY 


17 


IDATES(65) 


18 


***blank card to end job*** 



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D-1 



APPENDIX D 
Output Data 

The output from VBIII has been altered from the previous program. An 
extra line of output per day has been added, giving water contents of each 
zone on a percent volumetric basis, k-coeff icients and surface water. A table 
is provided to assist the user in interpreting the output. Table D-1 
considers the daily printout as a 20 column table with one or two lines per 
day. The second line is optional, depending on whether soil moisture contents 
are expressed on a percent volumetric basis. If soil depths, which are also 
optional, are not defined volumetric based moisture contents are not 
calculated. Table D-2 gives a sample of the printout. 

VBIII provides a second type of daily output, which is graphical. A 
simple line-printer plot of soil moisture on a volumetric basis is given for 
each zone. The symbols representing each zone are integers 1 to 6 for zones 1 
to 6, respectively. The dates (left hand columns) and daily precipitation 
(right hand column) are included in the plot. Table D-3 illustratesthe plot 
for four zones. Hand drawn lines joining the zone number symbols illustrate 
the plotted water contents. 

The plot option is controlled by parameters on Input Control 10. The 
scale of the soil moisture axis is controlled by SCL. The default, when SCL 
is zero, is one character per one percent volumetric. The number of 
characters which represent each percent volumetric water content is expanded 
by a factor of l.+SCL. The time scale is controlled by IHOR, which indicates 
the number of extra lines per day, or lines skipped between days. When 
IHOR = 0, no lines are skipped. To effectively use the plot option, the 
output unit for the plot (IP) must be different from the output unit for daily 
printouts (NP) (see Input Control 1). When no zone depths (OBDPTH) are 
specified, no plotting is done. 



D - 2 



TABLE D-1 



Line 1 



Guide to reading output 



COLUMN VARIABLE TYPE VARIABLE DEFINITION 



1 


integer 


2,3 


integer 


4 


integer 


5 


real 


6,7 


real 


8, 13 


real 


14, 15 


real 


16 


integer 


17 


integer 


18 


real 


19 


real 


20 


real 



year 

month, day 

crop stage number 

precipitation (rain or snow) 

PE, AE (mm) 

available moisture; zones 1 to 6 

(mm) 

runoff, drainage (mm) 

minimum daily temperature (^C) 

maximum daily temperature (^C) 

snow pack, can include melt 

water (mm) 

melt water (mm) 

water collected on the surface (mm) 



Line 2 



8, 13 real soil moisture; zones 1 to 6, 

expressed on a percent volumetric 
basis (mm) 

15, 20 real k-coeff icients used each day in 



zones 1 to 6, takes into account 
k-adjustments 



D - 3 



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E-1 
APPENDIX E.EVAPOTRANSPIRATION COEFFICIENTS (k,Z) 

Table E-1 . k-coefficients available for several crops and cropping practices 



Zone 



Stages 

Dormant season 
Growing season 



SS-FC 

FC-lst 

Ist-FC 

FC-2nd 

2nd-FC 



50 
55 



20 
19 



Brome grass 

.10 
.17 



.04 
.08 



,50 
,50 
,50 
,50 
,45 



20 
25 
22 
25 
25 



Alfalfa 

.15 
.23 
.18 
.25 
.20 



.12 
.22 
.15 
.20 
.20 



,02 
,03 



,08 
,15 
,15 
,18 
,20 



,01 
,01 



,05 
,10 
,10 
,12 
,15 



P-E 

E-F 

F-Pf 

Pf-Fe 

Fe-M 



Soybean^ 



20 
20 

15 
15 
10 



15 

20 

,15 

15 

,15 



.15 
.15 
.10 
.20 
.20 



.10 
.10 
.10 
.20 
.15 



,05 
,10 
,15 
,10 



,05 
,05 
,10 
,10 



P-E or R-P 

E-J 

J-H 

H-S 

S-R 



,40 
,40 
,40 
,40 
,40 



15 
20 
25 
30 
30 



Small grain 

.12 
.13 
.15 
.20 
.20 



.10 
.12 
.12 
.15 
.15 



,02 
,03 
,10 
,10 
,07 



,01 
,02 
,03 
,05 
,03 



P-E"" 

E-T 

T-Si 

Si-Ee 

Ee-H 



,40 
,40 
,40 
,40 
,40 



15 
20 
25 
30 
30 



Corn 

.12 
.13 
.15 
.20 
.20 



.10 
.12 
.12 
.15 
.15 



,02 
,03 
,10 
,10 
,07 



,01 
,02 
,03 
,05 
,03 



Symbols used to define stages: SS, start of growing season; FC, full cover; 
1st, first cut; 2nd, second cut; P, planting; E, emergence; F, flowering; 
Pf, pod filling; Fe, end of flowering; M, maturity; R, ripening; J, jointing; 
H, heading; S, soft dough; T, tasseling; Si, silking; Ee, ear emergence. 

Soybean statistics from Ravelo (1978) . 

'Small grain includes wheat, barley, and millet. 



Includes fallow or bare soil. 



E-2 

Table £-2. z- tables used in the VB accounting for different moisture release 
characteristics 



99.99 


50.00 


33.00 


25.00 


20.00 


A 
16.66 


14.28 


12.50 


11.11 


10.00 


9.09 


8.33 


7.69 


7.14 


6.67 


6.25 


5.88 


5.56 


5.26 


5.00 


4.76 


4.55 


4.35 


4.17 


4.00 


3.85 


3.70 


3.57 


3.45 


3.33 


3.23 


3.13 


3.30 


2.94 


2.86 


2.78 


2.70 


2.63 


2.56 


2.50 


2. 44 


2.38 


2.33 


2.27 


2.22 


2.17 


2.13 


2.08 


2.04 


2.00 


1.96 


1.92 


1.89 


1.85 


1.82 


1.79 


1.75 


1.72 


1.69 


1.67 


1.64 


1.61 


1.59 


1.56 


1.54 


1.52 


1.49 


1.47 


1.45 


1.43 


1.41 


1.39 


1.37 


1.35 


1.33 


1.32 


1.30 


1.28 


1.27 


1.25 


1.23 


1.22 


1.20 


1.19 


1.18 


1.16 


1.15 


1.14 


1.12 


1.11 


1.10 


1.09 


1.08 


1.06 


1.05 


1.04 

B 
7.16 


1.03 


1.02 


1.01 


1.00 


10.00 


10.00 


8.33 


7.50 


7.40 


7.00 


6.87 


6.44 


6.00 


5.81 


5.58 


5.38 


5.21 


5.00 


4.75 


4.52 


4.33 


4.16 


4.00 


3.86 


3.73 


3.60 


3.50 


3.40 


3.31 


3.22 


3.14 


3.07 


2.97 


2.90 


2.81 


2.76 


2.68 


2.63 


2.55 


2.49 


2.44 


2.38 


2.33 


2.26 


2.24 


2.18 


2.13 


2.11 


2.06 


2.02 


1.97 


1.94 


1.92 


1.88 


1.85 


1.81 


1.78 


1.76 


1.73 


1.70 


1.67 


1.66 


1.63 


1.60 


1.58 


1.56 


1.53 


1.52 


1.50 


1.47 


1.46 


1.43 


1.41 


1.40 


1.38 


1.36 


1.35 


1.33 


1.31 


1.29 


1.28 


1.26 


1.25 


1.23 


1.21 


1.20 


1.19 


1.17 


1.16 


1.14 


1.13 


1.12 


1.11 


1.09 


1.08 


1.07 


1.06 


1.05 


1.04 

C 
1.00 


1.03 


1.02 


1.01 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 


1.00 
D 
.16 


1.00 


1.00 


1.00 


1.00 


.00 


.00 


.00 


.00 


.20 


.14 


.13 


.11 


.10 


.09 


.16 


.15 


.14 


.13 


.13 


.12 


.17 


.16 


.15 


.14 


.18 


.17 


.21 


.20 


.19 


.22 


.21 


.24 


.23 


.26 


.25 


.27 


.29 


.29 


.30 


.32 


.34 


.36 


.35 


.36 


.37 


.37 


.39 


.40 


.41 


.43 


.46 


.47 


.48 


.49 


.50 


.54 


.56 


.56 


.59 


.61 


.64 


.66 


.68 


.70 


.76 


.79 


.83 


.85 


.91 


1.00 


1.03 


1.16 


1.41 


1.40 


1.38 


1.35 


1.34 


1.33 


1.31 


1.29 


1.28 


1.26 


1.25 


1.23 


1.21 


1.19 


1.18 


1.17 


1.15 


1.14 


1.13 


1.12 


1.11 


1.10 


1.08 


1.07 


1.06 


1.05 


1.04 


1.03 


1.02 


1.01 


1.00 



:ontinued next page 



E-3 



Table E-2 (continued) 



.02 


.04 


.06 


.08 


E 
.10 


.12 


.14 


.16 


.18 


.20 


.21 


.23 


.25 


.27 


.29 


.31 


.32 


.34 


.36 


.38 


.40 


.42 


.44 


.46 


.48 


.50 


.52 


.54 


.56 


.60 


.65 


.65 


.70 


.71 


.74 


.78 


.81 


.91 


1.05 


1.25 


1.34 


1.43 


1.63 


1.82 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


1.96 


1.92 


1.89 


1.85 


1.82 


1.79 


1.75 


1.72 


1.69 


1.67 


1.64 


1.61 


1.59 


1.56 


1.54 


1.52 


1.49 


1.47 


1.45 


1.43 


1.41 


1.39 


1.37 


1.35 


1.33 


1.32 


1.30 


1.28 


1.27 


1.25 


1.23 


1.22 


1.20 


1.19 


1.18 


1.16 


1.15 


1.14 


1.12 


1.11 


1.10 


1.09 


1.08 


1.06 


1.05 

F 
.60 


1.04 


1.03 


1.02 


1.01 


1.00 


1.00 


.75 


.66 


.50 


.66 


.85 


1.12 


1.44 


1.66 


1.82 


2.33 


2.69 


3.00 


3.33 


3.43 


3.70 


3.89 


4.00 


4.00 


4.00 


4.00 


4.00 


3.91 


3.80 


3.69 


3.59 


3.50 


3.41 


3.33 


3.20 


3.10 


3.00 


3.92 


2.85 


2.77 


2.69 


2.60 


2.55 


2.50 


2.45 


2.37 


2.30 


2.26 


2.22 


2.16 


2.10 


2.07 


2.04 


2.00 


1.95 


1.90 


1.86 


1.83 


1.80 


1.77 


1.75 


1.72 


1.69 


1.66 


1.63 


1.60 


1.58 


1.56 


1.53 


1.51 


1.49 


1.47 


1.45 


1.42 


1.40 


1.38 


1.36 


1.34 


1.32 


1.30 


1.28 


1.27 


1.26 


1.25 


1.23 


1.21 


1.19 


1.18 


1.17 


1.15 


1.14 


1.13 


1.12 


1.11 


1.10 


1.09 


1.08 


1.06 


1.05 

G 
1.43 


1.04 


1.03 


1.02 


1.01 


1.00 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.43 


1.40 


1.38 


1.35 


1.34 


1.33 


1.31 


1.29 


1.28 


1.26 


1.25 


1.23 


1.21 


1.19 


1.18 


1.17 


1.15 


1.14 


1.13 


1.12 


1.11 


1.10 


1.08 


1.07 


1.06 


1.05 

H 
2.00 


1.04 


1.03 


1.02 


1.01 


1.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


2.00 


1.96 


1.92 


1.88 


1.85 


1.81 


1.78 


1.75 


1.72 


1.69 


1.67 


1.64 


1.61 


1.59 


1.56 


1.53 


1.52 


1.49 


1.47 


1.45 


1.43 


1.40 


1.38 


1.35 


1.34 


1.33 


1.31 


1.29 


1.28 


1.26 


1.25 


1.23 


1.21 


1.19 


1.18 


1.17 


1.15 


1.14 


1.13 


1.12 


1.11 


1.10 


1.08 


1.07 


1.06 


1.05 


1.04 


1.03 


1.02 


1.01 


1.00 



I